Linguistics. Artificial intelligence. Related fields. Computation.

This article discusses the extension of ViewGen, an algorithm derived for belief ascription, to the areas of intensional object identification and metaphor. ViewGen represents the beliefs of agents as explicit, partitioned proposition sets known as environments. Environments are convenient, even essential, for addressing important pragmatic issues of reasoning. The article concentrates on showing that the transformation of information in metaphors, intensional object identification, and ordinary, nonmetaphorical belief ascription can all be seen as different manifestations of a single environment-amalgamation process. The (...) article also briefly discusses the extension of ViewGen to speech-act processing and the addition of a heuristic-based relevance-determination procedure, and justifies the partitioning approach to belief ascription. (shrink)

This outstanding collection is designed to address the fundamental issues and principles underlying the task of Artificial Intelligence.

What will it be like to admit Artificial Companions into our society? How will they change our relations with each other? How important will they be in the emotional and practical lives of their owners since we know that people became emotionally dependent even on simple devices like the Tamagotchi? How much social life might they have in contacting each other? The contributors to this book discuss the possibility and desirability of some form of long-term computer Companions now being a (...) certainty in the coming years. It is a good moment to consider, from a set of wide interdisciplinary perspectives, both how we shall construct them technically as well as their personal philosophical and social consequences. By Companions we mean conversationalists or confidants not robots but rather computer software agents whose function will be to get to know their owners over a long period. Those may well be elderly or lonely, and the contributions in the book focus not only on assistance via the internet but also on providing company and Companionship, by offering aspects of real personalization.". (shrink)

Machines are increasingly involved in decisions with ethical implications, which require ethical explanations. Current machine learning algorithms are ethically inscrutable, but not in a way very different from human behavior. This article looks at the role of rationality and reasoning in traditional ethical thought and in artificial intelligence, emphasizing the need for some explainability of actions. It then explores Neil Lawrence's embodiment factor as an insightful way of looking at the differences between human and machine intelligence, connecting it to the (...) theological understanding of embodiment, relationality, and personhood. Finally, it proposes the notion of artificial moral orthoses, which could provide ethical explanations for both artificial and human agents, as a more promising unifying approach to human and machine ethics. (shrink)

The paper describes a system for dealing with nestings of belief in terms of the mechanism of computational environment. A method is offered for computing the beliefs of A about B (and so on) in terms of the systems existing knowledge structures about A and B separately. A proposal for belief percolation is put forward: percolation being a side effect of the process of the computation of nested beliefs, but one which could explain the acquisition of unsupported beliefs. It is (...) argued that the mechanism proposed is compatible with a general least effort hypothesis concerning human mental functioning. (shrink)

A proposal is made concerning the introduction of the notions of cause and reason into a natural language understanding system. Its hypothesis is that one should prefer rational explanations of actions when dealing with human, or human‐like, agents, if one can find them in what one is analyzing, but that in other, nonhuman, cases one should prefer causal explanations. The reader is reminded of the existing state of the preference semantics system, and then are described the changes that would have (...) to be made to incorporate the above proposal and to find examples to test it. Finally, it is argued that there is no reason to think that a more complex taxonomy of causes (beyond the opposition of causes and reasons) is required, at least not if, as here. one seeks a procedural distinction corresponding to any taxonomic distinction among inference rules. (shrink)

We introduce the CUBISM system for the analysis and deep understanding of multi-participant dialogues. CUBISM brings together two typically separate forms of discourse analysis: semantic analysis and sociolinguistic analysis. In the paper proper, we describe and illustrate major components of the CUBISM system, and discuss the challenge posed by the system’s ultimate purpose, which is to automatically detect anomalous changes in participants’ expressed or implied beliefs about the world and each other, including shifts toward or away from cultural and community (...) norms. (shrink)

We introduce the CUBISM system for the analysis and deep understanding of multi-participant dialogues. CUBISM brings together two typically separate forms of discourse analysis: semantic analysis and sociolinguistic analysis. In the paper proper, we describe and illustrate major components of the CUBISM system, and discuss the challenge posed by the system’s ultimate purpose, which is to automatically detect anomalous changes in participants’ expressed or implied beliefs about the world and each other, including shifts toward or away from cultural and community (...) norms. (shrink)

I discuss two questions: (1) would Duhem have accepted the thesis of the continuity of scientific methodology? and (2) to what extent is the Oxford tradition of classification/subalternation of sciences continuous with early modern science? I argue that Duhem would have been surprised by the claim that scientific methodology is continuous; he expected at best only a continuity of physical theories, which he was trying to isolate from the perpetual fluctuations of methods and metaphysics. I also argue that the evidence (...) does not support the conclusion that early modern doctrines about mathematics and physics are continuous with the subalternation of sciences from Grosseteste, Bacon, and the theologians of fourteenth-century Oxford. The official and dominant context for early modern scientific methodology seems to have been progressive Thomism, and early modern thinkers seem to have pitted themselves against it. (shrink)

Nothing in McKay & Dennett's (M&D's) target article deals with the issue of how the adaptivity, or some other aspect, of beliefs might become a biological adaptation; which is to say, how the functions discussed might be coded in such a way in the brain that their development was also coded in gametes or sex transmission cells.

When John von Neumann turned his interest to computers, he was one of the leading mathematicians of his time. In the 1940s, he helped design two of the first stored-program digital electronic computers. He authored reports explaining the functional organization of modern computers for the first time, thereby influencing their construction worldwide (von Neumann, 1945; Burks et al., 1946). In the first of these reports, von Neumann described the computer as analogous to a brain, with an input “organ” (analogous to (...) sensory neurons), a memory, an arithmetical and a logical “organ” (analogous to associative neurons), and an output “organ” (analogous to motor neurons). His experience with computers convinced him that brains and computers, both having to do with the processing of information, should be studied by a new discipline–automata theory. In fact, according to von Neumann, automata theory would cover not only computers and brains, but also any biological or artificial systems that dealt with information and control, including robots and genes. Von Neumann never formulated a full-blown mathematical theory of automata, but he wrote several important exploratory papers (von Neumann, 1951, 1956, 1966). Meanwhile, besides designing hardware, he developed some of the first programs, programming languages, programming techniques, and numerical methods for solving mathematical problems using computers. (Much of his work on computing is reprinted in Aspray and Burks, 1987.) Shortly before his death in 1956, he wrote an informal synthesis of his views about brains. Though von Neumann left his manuscript sketchy and unfinished, Yale University Press published it as The Com- puter and the Brain in 1958. The 2000 reprint of this small but informative book is an opportunity to learn, or be reminded of, von Neumann’s thoughts on the computational organization of the mind-brain. Von Neumann began by explaining computers, which for him were essentially number crunchers: to compute was “to operate on .. (shrink)

As philosophers of mind we seem to hold in common no very clear view about the relevance that work in psychology or the neurosciences may or may not have to our own favourite questions—even if we call the subject ‘philosophical psychology’. For example, in the literature we find articles on pain some of which do, some of which don't, rely more or less heavily on, for example, the work of Melzack and Wall; the puzzle cases used so extensively in discussions (...) of personal identity are drawn sometimes from the pleasant exercise of scientific fantasy, at times from surprising reports of scientific fact; and there are those who deny, as well as those who affirm, the importance of the discovery of rapid-eye-movement sleep to the philosophical treatment of dreaming. A general account of the relation between scientific, and philosophical, psychology is long overdue and of the first importance. Here I shall limit myself to just one area where the two seem to connect, discussing one type of neuropsychological research and its relevance to questions in the philosophy of mind and the philosophy of psychology. (shrink)